Weakly supervised video anomaly detection. VAD [rgb]0,0,0aims to detect anomaly events in a given video and has been researched for years[1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}.
[rgb]0,0,0Unsupervised learning methods [1]}, [2]}, [4]}, [15]}, [5]}, [6]}, [8]}, [10]} 
[rgb]0.,0.,0 encode the usual pattern with only normal training samples and then detect the distinctive encoded patterns as anomalies.
Weakly supervised learning methods [20]}, [9]}, [22]}, [23]}, [11]} [rgb]0,0.,0with video-level labels are more applicable to distinguish abnormal events and normal events.
Existing weakly supervised VAD methods can be categorized into two classes, , encoder-agnostic methods and encoder-based methods. 1) Encoder-agnostic methods train only the classifier. Sultani et al. [20]} proposed a deep MIL ranking framework to detect anomalies; Zhang et al. [22]} further introduced inner-bag score gap regularization; Wan et al. [11]} introduced dynamic MIL loss and center-guided regularization.
2) Encoder-based methods train both a feature encoder and a classifier. Zhu et al. [23]} proposed an attention based MIL model combined with a optical flow based auto-encoder to encode motion-aware features.
Zhong et al. [9]} took weakly supervised VAD as a label noise learning task and proposed GCNs to filter label noise for iterative model training, [rgb]0,0,0 but the iterative optimization was inefficient and progressed slowly.
[rgb]0,0,0Some works focus on detecting anomalies in an offline manner [30]}, [31]} or a coarse-grained manner [20]}, [22]}, [23]}, [30]}, [31]}, which do not meet the real-time monitoring requirements for real-world applications.
